suvayu/dabl-test.ipynb

## dabl-test.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import dabl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"/scratch/data/kaggle/titanic/train.csv\")\n",
    "df2 = dabl.clean(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target looks like classification\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for /: 'str' and 'int'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-3c79ff0d023d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdabl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot_supervised\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Survived\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~/build/data-an/dabl/dabl/plot/supervised.py\u001b[0m in \u001b[0;36mplot_supervised\u001b[1;34m(X, target_col, types, scatter_alpha, verbose)\u001b[0m\n\u001b[0;32m    411\u001b[0m         melted = counts.T.melt().rename(\n\u001b[0;32m    412\u001b[0m             columns={'variable': 'class', 'value': 'count'})\n\u001b[1;32m--> 413\u001b[1;33m         \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbarplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'class'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'count'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmelted\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    414\u001b[0m         \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Target distribution\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    415\u001b[0m         plot_classification_continuous(X, target_col, types=types,\n",
      "\u001b[1;32m~/.local/lib/python3.6/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mbarplot\u001b[1;34m(x, y, hue, data, order, hue_order, estimator, ci, n_boot, units, orient, color, palette, saturation, errcolor, errwidth, capsize, dodge, ax, **kwargs)\u001b[0m\n\u001b[0;32m   3147\u001b[0m                           \u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mci\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munits\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3148\u001b[0m                           \u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3149\u001b[1;33m                           errcolor, errwidth, capsize, dodge)\n\u001b[0m\u001b[0;32m   3150\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3151\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~/.local/lib/python3.6/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, x, y, hue, data, order, hue_order, estimator, ci, n_boot, units, orient, color, palette, saturation, errcolor, errwidth, capsize, dodge)\u001b[0m\n\u001b[0;32m   1607\u001b[0m                                  order, hue_order, units)\n\u001b[0;32m   1608\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestablish_colors\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1609\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestimate_statistic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mci\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1610\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1611\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdodge\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdodge\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~/.local/lib/python3.6/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mestimate_statistic\u001b[1;34m(self, estimator, ci, n_boot)\u001b[0m\n\u001b[0;32m   1491\u001b[0m                     \u001b[0mstatistic\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1492\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1493\u001b[1;33m                     \u001b[0mstatistic\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstat_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1494\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1495\u001b[0m                 \u001b[1;31m# Get a confidence interval for this estimate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~/.local/lib/python3.6/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mmean\u001b[1;34m(a, axis, dtype, out, keepdims)\u001b[0m\n\u001b[0;32m   3116\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3117\u001b[0m     return _methods._mean(a, axis=axis, dtype=dtype,\n\u001b[1;32m-> 3118\u001b[1;33m                           out=out, **kwargs)\n\u001b[0m\u001b[0;32m   3119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3120\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~/.local/lib/python3.6/site-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_mean\u001b[1;34m(a, axis, dtype, out, keepdims)\u001b[0m\n\u001b[0;32m     85\u001b[0m             \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mret\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mrcount\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     86\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 87\u001b[1;33m         \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mret\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mrcount\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     88\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     89\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for /: 'str' and 'int'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dabl.plot_supervised(df2, \"Survived\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target looks like classification\n",
      "baseline score: 0.500\n",
      "baseline score: 0.500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/user/.local/lib/python3.6/site-packages/sklearn/feature_selection/mutual_info_.py:251: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if discrete_features == 'auto':\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 411.875x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 0x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dabl.plot_supervised(df, \"Survived\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}